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Zhang B, Lin S, Moraes L, Firkins J, Hristov AN, Kebreab E, Janssen PH, Bannink A, Bayat AR, Crompton LA, Dijkstra J, Eugène MA, Kreuzer M, McGee M, Reynolds CK, Schwarm A, Yáñez-Ruiz DR, Yu Z. Methane prediction equations including genera of rumen bacteria as predictor variables improve prediction accuracy. Sci Rep 2023; 13:21305. [PMID: 38042941 PMCID: PMC10693554 DOI: 10.1038/s41598-023-48449-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Accepted: 11/27/2023] [Indexed: 12/04/2023] Open
Abstract
Methane (CH4) emissions from ruminants are of a significant environmental concern, necessitating accurate prediction for emission inventories. Existing models rely solely on dietary and host animal-related data, ignoring the predicting power of rumen microbiota, the source of CH4. To address this limitation, we developed novel CH4 prediction models incorporating rumen microbes as predictors, alongside animal- and feed-related predictors using four statistical/machine learning (ML) methods. These include random forest combined with boosting (RF-B), least absolute shrinkage and selection operator (LASSO), generalized linear mixed model with LASSO (glmmLasso), and smoothly clipped absolute deviation (SCAD) implemented on linear mixed models. With a sheep dataset (218 observations) of both animal data and rumen microbiota data (relative sequence abundance of 330 genera of rumen bacteria, archaea, protozoa, and fungi), we developed linear mixed models to predict CH4 production (g CH4/animal·d, ANIM-B models) and CH4 yield (g CH4/kg of dry matter intake, DMI-B models). We also developed models solely based on animal-related data. Prediction performance was evaluated 200 times with random data splits, while fitting performance was assessed without data splitting. The inclusion of microbial predictors improved the models, as indicated by decreased root mean square prediction error (RMSPE) and mean absolute error (MAE), and increased Lin's concordance correlation coefficient (CCC). Both glmmLasso and SCAD reduced the Akaike information criterion (AIC) and Bayesian information criterion (BIC) for both the ANIM-B and the DMI-B models, while the other two ML methods had mixed outcomes. By balancing prediction performance and fitting performance, we obtained one ANIM-B model (containing 10 genera of bacteria and 3 animal data) fitted using glmmLasso and one DMI-B model (5 genera of bacteria and 1 animal datum) fitted using SCAD. This study highlights the importance of incorporating rumen microbiota data in CH4 prediction models to enhance accuracy and robustness. Additionally, ML methods facilitate the selection of microbial predictors from high-dimensional metataxonomic data of the rumen microbiota without overfitting. Moreover, the identified microbial predictors can serve as biomarkers of CH4 emissions from sheep, providing valuable insights for future research and mitigation strategies.
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Affiliation(s)
- Boyang Zhang
- Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA
| | - Shili Lin
- Department of Statistics, The Ohio State University, 2029 Fyffe Road, Columbus, OH, 43210, USA.
| | - Luis Moraes
- Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA
- Consultoria, Piracicaba, SP, Brazil
| | - Jeffrey Firkins
- Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA
| | - Alexander N Hristov
- Department of Animal Science, The Pennsylvania State University, University Park, PA, USA
| | - Ermias Kebreab
- Department of Animal Science, University of California, Davis, CA, USA
| | - Peter H Janssen
- AgResearch Limited, Grasslands Research Centre, Palmerston North, 4442, New Zealand
| | - André Bannink
- Wageningen Livestock Research, Wageningen University & Research, Wageningen, The Netherlands
| | - Alireza R Bayat
- Milk Production, Production Systems, Natural Resources Institute Finland (Luke), 31600, Jokioinen, Finland
| | - Les A Crompton
- School of Agriculture, Policy, and Development, University of Reading, Reading, UK
| | - Jan Dijkstra
- Animal Nutrition Group, Wageningen University & Research, Wageningen, The Netherlands
| | - Maguy A Eugène
- INRAE UMR Herbivores, VetAgro Sup, Université Clermont Auvergne, Saint-Genès-Champanelle, France
| | - Michael Kreuzer
- Institute of Agricultural Sciences, ETH Zurich, Zurich, Switzerland
| | - Mark McGee
- Teagasc, AGRIC, Grange, Dunsany., CO., Meath, Ireland
| | | | - Angela Schwarm
- Department of Animal and Aquacultural Sciences, Norwegian University of Life Sciences, Ås, Norway
| | | | - Zhongtang Yu
- Department of Animal Sciences, The Ohio State University, Columbus, OH, 43210, USA.
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Niu M, Kebreab E, Hristov AN, Oh J, Arndt C, Bannink A, Bayat AR, Brito AF, Boland T, Casper D, Crompton LA, Dijkstra J, Eugène MA, Garnsworthy PC, Haque MN, Hellwing ALF, Huhtanen P, Kreuzer M, Kuhla B, Lund P, Madsen J, Martin C, McClelland SC, McGee M, Moate PJ, Muetzel S, Muñoz C, O'Kiely P, Peiren N, Reynolds CK, Schwarm A, Shingfield KJ, Storlien TM, Weisbjerg MR, Yáñez‐Ruiz DR, Yu Z. Prediction of enteric methane production, yield, and intensity in dairy cattle using an intercontinental database. Glob Chang Biol 2018; 24:3368-3389. [PMID: 29450980 PMCID: PMC6055644 DOI: 10.1111/gcb.14094] [Citation(s) in RCA: 114] [Impact Index Per Article: 19.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2017] [Revised: 12/15/2017] [Accepted: 01/29/2018] [Indexed: 05/13/2023]
Abstract
Enteric methane (CH4 ) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH4 is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH4 production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH4 production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH4 production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross-validate their performance; and (4) assess the trade-off between availability of on-farm inputs and CH4 prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH4 production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH4 emission conversion factors for specific regions are required to improve CH4 production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH4 yield and intensity prediction, information on milk yield and composition is required for better estimation.
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Affiliation(s)
- Mutian Niu
- Department of Animal ScienceUniversity of CaliforniaDavisCAUSA
| | - Ermias Kebreab
- Department of Animal ScienceUniversity of CaliforniaDavisCAUSA
| | - Alexander N. Hristov
- Department of Animal ScienceThe Pennsylvania State UniversityUniversity ParkPAUSA
| | - Joonpyo Oh
- Department of Animal ScienceThe Pennsylvania State UniversityUniversity ParkPAUSA
| | | | - André Bannink
- Wageningen Livestock ResearchWageningen University & ResearchWageningenThe Netherlands
| | - Ali R. Bayat
- Milk Production Solutions, Green TechnologyNatural Resources Institute Finland (Luke)JokioinenFinland
| | - André F. Brito
- Department of Agriculture, Nutrition and Food SystemsUniversity of New HampshireDurhamNHUSA
| | - Tommy Boland
- School of Agriculture and Food ScienceUniversity College DublinBelfield, Dublin 4Ireland
| | | | - Les A. Crompton
- School of Agriculture, Policy and DevelopmentUniversity of ReadingReadingUK
| | - Jan Dijkstra
- Animal Nutrition GroupWageningen University & ResearchWageningenThe Netherlands
| | - Maguy A. Eugène
- UMR Herbivores, INRA, VetAgro Sup, Université Clermont AuvergneSaint‐Genès‐ChampanelleFrance
| | | | - Md Najmul Haque
- Department of Large Animal SciencesUniversity of CopenhagenCopenhagenDenmark
| | | | - Pekka Huhtanen
- Department of Agricultural Science for Northern SwedenSwedish University of Agricultural SciencesUmeåSweden
| | - Michael Kreuzer
- ETH ZurichInstitute of Agricultural SciencesZurichSwitzerland
| | - Bjoern Kuhla
- Institute of Nutritional PhysiologyLeibniz Institute for Farm Animal BiologyDummerstorfMecklenburg‐VorpommernGermany
| | - Peter Lund
- Department of Animal ScienceAarhus UniversityTjeleDenmark
| | - Jørgen Madsen
- Department of Large Animal SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Cécile Martin
- UMR Herbivores, INRA, VetAgro Sup, Université Clermont AuvergneSaint‐Genès‐ChampanelleFrance
| | | | - Mark McGee
- Teagasc, Agriculture and Food Development AuthorityCarlowIreland
| | - Peter J. Moate
- Agriculture Research DivisionDepartment of Economic Development, Jobs, Transport and ResourcesMelbourneVic.Australia
| | | | - Camila Muñoz
- Instituto de Investigaciones Agropecuarias, INIA RemehueOsornoChile
| | - Padraig O'Kiely
- Teagasc, Agriculture and Food Development AuthorityCarlowIreland
| | - Nico Peiren
- Animal Sciences DepartmentFlanders Research Institute for AgricultureFisheries and FoodMelleBelgium
| | | | - Angela Schwarm
- ETH ZurichInstitute of Agricultural SciencesZurichSwitzerland
| | - Kevin J. Shingfield
- Institute of Biological, Environmental and Rural SciencesAberystwyth UniversityAberystwythUK
| | - Tonje M. Storlien
- Department of Animal and Aquacultural SciencesNorwegian University of Life SciencesÅsNorway
| | | | | | - Zhongtang Yu
- Department of Animal SciencesThe Ohio State UniversityColumbusOHUSA
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